Three datasets in .CSV format were downloaded from the Alzheimer’s Disease Neuroimaging Initiative Study Data repository. ADNI data is freely accessible to all registered users.
Longitudinal 18F-AV-1451 tau-PET data was acquired from Study Data/Imaging/PET Image Analysis/UC Berkeley - AV1451 Analysis [ADNI2,3] (version: 5/12/2020). This CSV file contains 1,121 rows and 241 columns. Each row represents one tau-PET scan; some subjects had repeated scans separated by approximately one year, while other subjects had only one scan.
Columns include subject information including anonymized subject ID, visit code, and PET exam date. The other columns encode regional volume and tau-PET uptake. Specifically, there are 123 distinct cortical and subcortical regions of interest (ROIs), each of which has a volume field (in mm^3) and a tau-PET uptake field, called the Standardized Uptake Value Ratio (SUVR). The SUVR value is normalized to the tau-PET uptake in the inferior cerebellum gray matter, a commonly-used region for tau normalization given the lack of inferior cerebellar tau pathology in Alzheimer’s Disease. These 123 ROIs were delineated by first co-registering the tau-PET image to a high-resolution structural T1-weighted MPRAGE acquired in the same imaging session, and then applying FreeSurfer (v5.3) for automated regional segmentation and parcellation. Furthermore, to mitigate issues with lower voxel resolution in PET imaging, partial volume correction was applied to use probabilistic tissue segmentation maps to refine individual ROIs.
Note: these PET processing steps were all performed by Susan Landau, Deniz Korman, and William Jagust at the Helen Wills Neuroscience Institute, UC Berkeley and Lawrence Berkeley National Laboratory.
Longitudinal Alzheimer’s Disease Assessment Scale-13 (ADAS13) cognitive score dataset was downloaded from Study Data/Assessments/Neuropsychological/Alzheimer’s Disease Assessment Scale (ADAS) [ADNIGO,2,3]. This CSV file contains 6,695 rows and 121 columns. Each row represents one clinical visit; most subjects had several clinical visits separated by approximately one year each, though some subjects had only one clinical visit.
The ADAS13 score ranges from 0 to 70 and represents a composite score based on thirteen individual assessment components. A score of 0 reflects no cognitive impairment, while a score of 70 indicates severe cognitive impairment. There are multiple columns per individual ADAS component, indicating information such as cognitive task assessed, time to complete the task, and task completion score. There are also columns pertaining to subject/visit information, such as anonymized subject ID, visit code, site ID, ADNI project phase, and exam date.
The general cognitive status and cognitive diagnosis dataset was downloaded from Study Data/Assessments/Diagnosis/Diagnostic Summary [ADNI1,GO,2,3]. This CSV file contains 12,268 rows and 54 columns. Certain columns only pertain to certain subsets of the data depending on the project cohort (ADNI1, ADNI-GO, ADNI2, or ADNI3). There are columns for subject/visit information such as anonymized subject ID, ADNI project phase, and exam date, and the rest of the columns indicate cognitive diagnosis information such as probability of dementia due to AD, current cognitive diagnosis, and change in cognitive status from the previous visit. These metrics were all evaluated by neurologists.
library(tidyverse)
library(knitr)
library(kableExtra)
library(plotly)
library(lubridate)
library(forcats)og_tau <- read.csv("../../ADNI_Data/Raw_Data/UCBERKELEYAV1451_PVC_05_12_20.csv")
str(og_tau)## 'data.frame': 1120 obs. of 165 variables:
## $ RID : int 21 31 31 56 56 56 59 69 69 69 ...
## $ VISCODE : chr "init" "init" "y1" "init" ...
## $ VISCODE2 : chr "m144" "m144" "m156" "m144" ...
## $ EXAMDATE : chr "2/2/2018" "4/24/2018" "4/23/2019" "2/20/2018" ...
## $ INFERIOR_CEREBGM_SUVR : num 1.32 1.33 1.33 1.28 1.24 ...
## $ INFERIOR_CEREBGM_VOLUME : int 52306 54296 54296 56750 56750 56750 59836 56862 56862 56862 ...
## $ HEMIWM_SUVR : num 1.02 0.85 0.866 1.138 1.196 ...
## $ HEMIWM_VOLUME : int 321220 281690 281690 336495 336495 336495 294422 463900 463900 463900 ...
## $ BRAAK12_SUVR : num 2.06 2.24 2.3 1.91 1.88 ...
## $ BRAAK12_VOLUME : int 10275 7587 7587 9376 9376 9376 10379 10981 10981 10981 ...
## $ BRAAK34_SUVR : num 1.95 1.87 1.8 1.82 1.77 ...
## $ BRAAK34_VOLUME : int 95661 95419 95419 92482 92482 92482 94092 112788 112788 112788 ...
## $ BRAAK56_SUVR : num 1.99 1.92 1.84 1.87 1.84 ...
## $ BRAAK56_VOLUME : int 284821 288136 288136 283119 283119 283119 283727 325054 325054 325054 ...
## $ BRAIN_STEM_SUVR : num 1.27 1.12 1.12 1.2 1.17 ...
## $ BRAIN_STEM_VOLUME : int 16955 16952 16952 20508 20508 20492 18057 18872 18872 18866 ...
## $ LEFT_MIDDLEFR_SUVR : num 2.02 1.93 1.8 1.83 1.78 ...
## $ LEFT_MIDDLEFR_VOLUME : int 17640 18517 18517 17164 17164 17164 17683 21907 21907 21907 ...
## $ LEFT_ORBITOFR_SUVR : num 2.17 2.03 1.92 2.11 1.98 ...
## $ LEFT_ORBITOFR_VOLUME : int 11676 10091 10091 11721 11721 11721 10917 12109 12109 12109 ...
## $ LEFT_PARSFR_SUVR : num 2.02 2.01 1.98 2.03 1.99 ...
## $ LEFT_PARSFR_VOLUME : int 9201 7799 7799 9185 9185 9185 7709 9813 9813 9813 ...
## $ LEFT_ACCUMBENS_AREA_SUVR : num 1.14 1.04 1.79 1.12 1.18 ...
## $ LEFT_ACCUMBENS_AREA_VOLUME : int 500 318 318 308 308 308 353 361 361 361 ...
## $ LEFT_AMYGDALA_SUVR : num 1.31 1.54 1.63 1.42 1.37 ...
## $ LEFT_AMYGDALA_VOLUME : int 1367 1224 1224 1561 1561 1561 993 1499 1499 1499 ...
## $ LEFT_CAUDATE_SUVR : num 2.08 1.46 1.34 1.95 1.83 ...
## $ LEFT_CAUDATE_VOLUME : int 3016 4890 4890 3083 3083 3083 2874 4049 4049 4049 ...
## $ LEFT_HIPPOCAMPUS_SUVR : num 2.12 1.96 2.2 1.69 1.73 ...
## $ LEFT_HIPPOCAMPUS_VOLUME : int 3822 3050 3050 3476 3476 3476 3603 3550 3550 3550 ...
## $ LEFT_PALLIDUM_SUVR : num 3.79 1.89 1.95 2.5 2.6 ...
## $ LEFT_PALLIDUM_VOLUME : int 444 2066 2066 1301 1301 1301 1081 1634 1634 1634 ...
## $ LEFT_PUTAMEN_SUVR : num 1.69 1.64 1.42 1.9 1.78 ...
## $ LEFT_PUTAMEN_VOLUME : int 4000 5675 5675 4832 4832 4832 3563 4891 4891 4891 ...
## $ LEFT_THALAMUS_PROPER_SUVR : num 1.45 1.32 1.24 1.54 1.53 ...
## $ LEFT_THALAMUS_PROPER_VOLUME : int 8226 6195 6195 7114 7114 7114 7561 7518 7518 7518 ...
## $ RIGHT_MIDDLEFR_SUVR : num 2.08 1.91 1.8 1.94 1.85 ...
## $ RIGHT_MIDDLEFR_VOLUME : int 17250 18440 18440 15605 15605 15605 16280 22586 22586 22586 ...
## $ RIGHT_ORBITOFR_SUVR : num 2.19 2.01 1.86 2.17 2.03 ...
## $ RIGHT_ORBITOFR_VOLUME : int 11614 12637 12637 11064 11064 11064 11537 12575 12575 12575 ...
## $ RIGHT_PARSFR_SUVR : num 2.17 2.08 1.9 2.09 2.01 ...
## $ RIGHT_PARSFR_VOLUME : int 9255 8131 8131 9641 9641 9641 8839 9119 9119 9119 ...
## $ RIGHT_ACCUMBENS_AREA_SUVR : num 1.41 1.65 1.66 1.01 1.07 ...
## $ RIGHT_ACCUMBENS_AREA_VOLUME : int 545 413 413 423 423 423 542 528 528 528 ...
## $ RIGHT_AMYGDALA_SUVR : num 1.18 1.79 1.89 1.37 1.44 ...
## $ RIGHT_AMYGDALA_VOLUME : int 1268 1028 1028 1464 1464 1464 1313 1797 1797 1797 ...
## $ RIGHT_CAUDATE_SUVR : num 2.01 1.57 1.37 1.96 1.89 ...
## $ RIGHT_CAUDATE_VOLUME : int 3179 4854 4854 2984 2984 2984 3224 3835 3835 3835 ...
## $ RIGHT_HIPPOCAMPUS_SUVR : num 2.01 2.09 2.03 1.62 1.64 ...
## $ RIGHT_HIPPOCAMPUS_VOLUME : int 3978 2723 2723 3489 3489 3489 3667 3942 3942 3942 ...
## $ RIGHT_PALLIDUM_SUVR : num 3.01 2.32 2.12 2.33 2.48 ...
## $ RIGHT_PALLIDUM_VOLUME : int 846 1531 1531 1262 1262 1262 1088 1552 1552 1552 ...
## $ RIGHT_PUTAMEN_SUVR : num 1.68 1.62 1.53 2.06 1.94 ...
## $ RIGHT_PUTAMEN_VOLUME : int 4322 5774 5774 4328 4328 4328 3190 4569 4569 4569 ...
## $ RIGHT_THALAMUS_PROPER_SUVR : num 1.42 1.33 1.24 1.52 1.55 ...
## $ RIGHT_THALAMUS_PROPER_VOLUME : int 5968 5442 5442 5940 5940 5940 6257 7899 7899 7899 ...
## $ CHOROID_SUVR : num 7.45 4.56 4.31 3.84 3.79 ...
## $ CHOROID_VOLUME : int 4180 3591 3591 3165 3165 3165 3717 3663 3663 3663 ...
## $ CTX_LH_BANKSSTS_SUVR : num 1.75 1.49 1.6 1.7 1.63 ...
## $ CTX_LH_BANKSSTS_VOLUME : int 1553 1633 1633 1812 1812 1812 1694 2601 2601 2601 ...
## $ CTX_LH_CAUDALANTERIORCINGULATE_SUVR : num 1.67 1.73 1.65 1.69 1.69 ...
## $ CTX_LH_CAUDALANTERIORCINGULATE_VOLUME : int 1138 1387 1387 1124 1124 1124 1465 1512 1512 1512 ...
## $ CTX_LH_CUNEUS_SUVR : num 2.33 2.2 2.05 2.01 2 ...
## $ CTX_LH_CUNEUS_VOLUME : int 2023 2702 2702 2429 2429 2429 2393 2222 2222 2222 ...
## $ CTX_LH_ENTORHINAL_SUVR : num 2.07 2.3 2.43 2.79 2.52 ...
## $ CTX_LH_ENTORHINAL_VOLUME : int 1468 1035 1035 1068 1068 1068 1297 1888 1888 1888 ...
## $ CTX_LH_FUSIFORM_SUVR : num 1.97 1.87 1.83 1.84 1.77 ...
## $ CTX_LH_FUSIFORM_VOLUME : int 7956 6997 6997 7694 7694 7694 7807 9083 9083 9083 ...
## $ CTX_LH_INFERIORPARIETAL_SUVR : num 1.99 1.95 1.94 1.85 1.89 ...
## $ CTX_LH_INFERIORPARIETAL_VOLUME : int 11656 10174 10174 9243 9243 9243 8180 9846 9846 9846 ...
## $ CTX_LH_INFERIORTEMPORAL_SUVR : num 2.16 1.97 2.05 2.1 2.02 ...
## $ CTX_LH_INFERIORTEMPORAL_VOLUME : int 6606 6418 6418 7286 7286 7286 6869 9599 9599 9599 ...
## $ CTX_LH_INSULA_SUVR : num 1.51 1.64 1.65 1.51 1.48 ...
## $ CTX_LH_INSULA_VOLUME : int 6711 4654 4654 6003 6003 6003 5513 6597 6597 6597 ...
## $ CTX_LH_ISTHMUSCINGULATE_SUVR : num 1.9 1.81 1.82 1.79 1.94 ...
## $ CTX_LH_ISTHMUSCINGULATE_VOLUME : int 2283 2215 2215 1549 1549 1549 1944 2264 2264 2264 ...
## $ CTX_LH_LATERALOCCIPITAL_SUVR : num 2.39 2.06 1.99 1.92 2 ...
## $ CTX_LH_LATERALOCCIPITAL_VOLUME : int 8532 10148 10148 8292 8292 8292 10612 9404 9404 9404 ...
## $ CTX_LH_LINGUAL_SUVR : num 2.27 1.95 1.97 1.76 1.74 ...
## $ CTX_LH_LINGUAL_VOLUME : int 4329 4658 4658 5606 5606 5606 5435 6488 6488 6488 ...
## $ CTX_LH_MIDDLETEMPORAL_SUVR : num 2.2 2.06 1.89 2.04 1.99 ...
## $ CTX_LH_MIDDLETEMPORAL_VOLUME : int 7445 8322 8322 7292 7292 7292 8031 9467 9467 9467 ...
## $ CTX_LH_PARACENTRAL_SUVR : num 1.99 1.79 1.8 1.91 1.8 ...
## $ CTX_LH_PARACENTRAL_VOLUME : int 2672 2890 2890 3231 3231 3231 3358 3173 3173 3173 ...
## $ CTX_LH_PARAHIPPOCAMPAL_SUVR : num 1.6 1.86 1.92 1.72 1.66 ...
## $ CTX_LH_PARAHIPPOCAMPAL_VOLUME : int 1659 1549 1549 1900 1900 1900 1989 2296 2296 2296 ...
## $ CTX_LH_PERICALCARINE_SUVR : num 2.23 1.45 1.41 1.56 1.54 ...
## $ CTX_LH_PERICALCARINE_VOLUME : int 1678 2004 2004 1866 1866 1866 1918 1927 1927 1927 ...
## $ CTX_LH_POSTCENTRAL_SUVR : num 2.03 1.81 1.82 1.85 1.78 ...
## $ CTX_LH_POSTCENTRAL_VOLUME : int 8281 8428 8428 8275 8275 8275 7580 8976 8976 8976 ...
## $ CTX_LH_POSTERIORCINGULATE_SUVR : num 1.82 1.89 1.84 1.72 1.67 ...
## $ CTX_LH_POSTERIORCINGULATE_VOLUME : int 2439 2608 2608 2683 2683 2683 2573 2638 2638 2638 ...
## $ CTX_LH_PRECENTRAL_SUVR : num 1.91 1.85 1.75 1.62 1.61 ...
## $ CTX_LH_PRECENTRAL_VOLUME : int 11174 12349 12349 10924 10924 10924 10820 12307 12307 12307 ...
## $ CTX_LH_PRECUNEUS_SUVR : num 1.93 1.89 1.94 1.81 1.81 ...
## $ CTX_LH_PRECUNEUS_VOLUME : int 7870 8313 8313 8387 8387 8387 8311 8584 8584 8584 ...
## $ CTX_LH_ROSTRALANTERIORCINGULATE_SUVR : num 1.71 1.58 1.49 1.59 1.48 ...
## $ CTX_LH_ROSTRALANTERIORCINGULATE_VOLUME: int 2928 2448 2448 1695 1695 1695 2466 2915 2915 2915 ...
## $ CTX_LH_SUPERIORFRONTAL_SUVR : num 1.86 1.86 1.74 1.84 1.77 ...
## [list output truncated]og_tau %>%
select(RID, EXAMDATE) %>%
mutate(Scan_Date = as.Date(EXAMDATE, format="%m/%d/%Y")) %>%
plot_ly(x=~Scan_Date, type="histogram") %>%
layout(title = 'Tau-PET Scan Date Distribution',
xaxis = list(title = 'Scan Date',
zeroline = TRUE),
yaxis = list(title = 'Number of PET Scans')) p_num_long <- og_tau %>%
mutate(RID=as.character(RID)) %>%
group_by(RID) %>%
summarise(n_scans=n()) %>%
ggplot(., aes(x=fct_reorder(RID, n_scans, .desc=T), y=n_scans)) +
geom_bar(stat="identity", aes(fill=n_scans, color=n_scans)) +
labs(fill="Count", color="Count") +
ggtitle("Number of Longitudinal PET Scans per Subject") +
ylab("Number of PET Scans") +
xlab("Subject") +
theme(axis.text.x=element_blank(),
plot.title=element_text(hjust=0.5))
ggplotly(p_num_long)I am going to focus exclusively on subjects with at least two scans, to examine changes in tau accumulation over time.
og_tau %>%
mutate(RID=as.character(RID)) %>%
group_by(RID) %>%
summarise(n_scans=n()) %>%
filter(n_scans>=2) %>%
ungroup() %>%
summarise(`Number of Subjects`=n(),
`Number of Scans in Total`=sum(n_scans))## # A tibble: 1 x 2
## `Number of Subjects` `Number of Scans in Total`
## <int> <int>
## 1 249 593The ADNI tau-PET scans are intended to be spaced approximately one year apart for each subject, but the temporal distribution should still be examined:
p_pet_interval <- og_tau %>%
select(RID, EXAMDATE) %>%
mutate(Scan_Date = as.Date(EXAMDATE, format="%m/%d/%Y")) %>%
group_by(RID) %>%
mutate(n_scans=n()) %>%
filter(n_scans>=2) %>%
mutate(Years_between_Scans =
as.numeric((Scan_Date - lag(Scan_Date,
default = Scan_Date[1]))/365)) %>%
filter(Years_between_Scans>0) %>%
ggplot(., aes(x=Years_between_Scans)) +
geom_histogram(stat="count", color="dodgerblue3") +
ggtitle("Years in between Tau-PET Scans per Subject") +
ylab("Frequency") +
xlab("# Years between two consecutive scans for a subject") +
theme_minimal() +
theme(plot.title=element_text(hjust=0.5)) ## Warning: Ignoring unknown parameters: binwidth, bins, padggplotly(p_pet_interval)og_tau %>%
select(-VISCODE, -VISCODE2, -update_stamp) %>%
group_by(RID) %>%
mutate(n_scans=n()) %>%
filter(n_scans>=2) %>%
select(-n_scans) %>%
ungroup() %>%
select(!matches("VOLUME")) %>%
pivot_longer(cols=c(-RID, -EXAMDATE), names_to="ROI", values_to="SUVR") %>%
group_by(ROI) %>%
summarise(`Percent Missing` = sum(is.na(SUVR))/n(),
`Number Missing` = sum(is.na(SUVR))) %>%
kable(., booktabs=T)| ROI | Percent Missing | Number Missing |
|---|---|---|
| BRAAK12_SUVR | 0 | 0 |
| BRAAK34_SUVR | 0 | 0 |
| BRAAK56_SUVR | 0 | 0 |
| BRAIN_STEM_SUVR | 0 | 0 |
| CHOROID_SUVR | 0 | 0 |
| CTX_LH_BANKSSTS_SUVR | 0 | 0 |
| CTX_LH_CAUDALANTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_LH_CUNEUS_SUVR | 0 | 0 |
| CTX_LH_ENTORHINAL_SUVR | 0 | 0 |
| CTX_LH_FUSIFORM_SUVR | 0 | 0 |
| CTX_LH_INFERIORPARIETAL_SUVR | 0 | 0 |
| CTX_LH_INFERIORTEMPORAL_SUVR | 0 | 0 |
| CTX_LH_INSULA_SUVR | 0 | 0 |
| CTX_LH_ISTHMUSCINGULATE_SUVR | 0 | 0 |
| CTX_LH_LATERALOCCIPITAL_SUVR | 0 | 0 |
| CTX_LH_LINGUAL_SUVR | 0 | 0 |
| CTX_LH_MIDDLETEMPORAL_SUVR | 0 | 0 |
| CTX_LH_PARACENTRAL_SUVR | 0 | 0 |
| CTX_LH_PARAHIPPOCAMPAL_SUVR | 0 | 0 |
| CTX_LH_PERICALCARINE_SUVR | 0 | 0 |
| CTX_LH_POSTCENTRAL_SUVR | 0 | 0 |
| CTX_LH_POSTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_LH_PRECENTRAL_SUVR | 0 | 0 |
| CTX_LH_PRECUNEUS_SUVR | 0 | 0 |
| CTX_LH_ROSTRALANTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_LH_SUPERIORFRONTAL_SUVR | 0 | 0 |
| CTX_LH_SUPERIORPARIETAL_SUVR | 0 | 0 |
| CTX_LH_SUPERIORTEMPORAL_SUVR | 0 | 0 |
| CTX_LH_SUPRAMARGINAL_SUVR | 0 | 0 |
| CTX_LH_TEMPORALPOLE_SUVR | 0 | 0 |
| CTX_LH_TRANSVERSETEMPORAL_SUVR | 0 | 0 |
| CTX_RH_BANKSSTS_SUVR | 0 | 0 |
| CTX_RH_CAUDALANTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_RH_CUNEUS_SUVR | 0 | 0 |
| CTX_RH_ENTORHINAL_SUVR | 0 | 0 |
| CTX_RH_FUSIFORM_SUVR | 0 | 0 |
| CTX_RH_INFERIORPARIETAL_SUVR | 0 | 0 |
| CTX_RH_INFERIORTEMPORAL_SUVR | 0 | 0 |
| CTX_RH_INSULA_SUVR | 0 | 0 |
| CTX_RH_ISTHMUSCINGULATE_SUVR | 0 | 0 |
| CTX_RH_LATERALOCCIPITAL_SUVR | 0 | 0 |
| CTX_RH_LINGUAL_SUVR | 0 | 0 |
| CTX_RH_MIDDLETEMPORAL_SUVR | 0 | 0 |
| CTX_RH_PARACENTRAL_SUVR | 0 | 0 |
| CTX_RH_PARAHIPPOCAMPAL_SUVR | 0 | 0 |
| CTX_RH_PERICALCARINE_SUVR | 0 | 0 |
| CTX_RH_POSTCENTRAL_SUVR | 0 | 0 |
| CTX_RH_POSTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_RH_PRECENTRAL_SUVR | 0 | 0 |
| CTX_RH_PRECUNEUS_SUVR | 0 | 0 |
| CTX_RH_ROSTRALANTERIORCINGULATE_SUVR | 0 | 0 |
| CTX_RH_SUPERIORFRONTAL_SUVR | 0 | 0 |
| CTX_RH_SUPERIORPARIETAL_SUVR | 0 | 0 |
| CTX_RH_SUPERIORTEMPORAL_SUVR | 0 | 0 |
| CTX_RH_SUPRAMARGINAL_SUVR | 0 | 0 |
| CTX_RH_TEMPORALPOLE_SUVR | 0 | 0 |
| CTX_RH_TRANSVERSETEMPORAL_SUVR | 0 | 0 |
| HEMIWM_SUVR | 0 | 0 |
| INFERIOR_CEREBGM_SUVR | 0 | 0 |
| LEFT_ACCUMBENS_AREA_SUVR | 0 | 0 |
| LEFT_AMYGDALA_SUVR | 0 | 0 |
| LEFT_CAUDATE_SUVR | 0 | 0 |
| LEFT_HIPPOCAMPUS_SUVR | 0 | 0 |
| LEFT_MIDDLEFR_SUVR | 0 | 0 |
| LEFT_ORBITOFR_SUVR | 0 | 0 |
| LEFT_PALLIDUM_SUVR | 0 | 0 |
| LEFT_PARSFR_SUVR | 0 | 0 |
| LEFT_PUTAMEN_SUVR | 0 | 0 |
| LEFT_THALAMUS_PROPER_SUVR | 0 | 0 |
| OTHER_SUVR | 0 | 0 |
| RIGHT_ACCUMBENS_AREA_SUVR | 0 | 0 |
| RIGHT_AMYGDALA_SUVR | 0 | 0 |
| RIGHT_CAUDATE_SUVR | 0 | 0 |
| RIGHT_HIPPOCAMPUS_SUVR | 0 | 0 |
| RIGHT_MIDDLEFR_SUVR | 0 | 0 |
| RIGHT_ORBITOFR_SUVR | 0 | 0 |
| RIGHT_PALLIDUM_SUVR | 0 | 0 |
| RIGHT_PARSFR_SUVR | 0 | 0 |
| RIGHT_PUTAMEN_SUVR | 0 | 0 |
| RIGHT_THALAMUS_PROPER_SUVR | 0 | 0 |
p_roi_suvr <- og_tau %>%
select(-VISCODE, -VISCODE2, -update_stamp) %>%
group_by(RID) %>%
mutate(n_scans=n()) %>%
filter(n_scans>=2) %>%
select(-n_scans) %>%
select(!matches("VOLUME")) %>%
pivot_longer(cols=c(-RID, -EXAMDATE), names_to="ROI", values_to="SUVR") %>%
mutate(ROI = str_replace(ROI, "_SUVR", "")) %>%
group_by(ROI) %>%
summarise(Mean_SUVR=mean(SUVR, na.rm=T),
SD_SUVR = sd(SUVR, na.rm=T),
ymin = Mean_SUVR-SD_SUVR,
ymax = Mean_SUVR+SD_SUVR) %>%
ggplot(data=., mapping=aes(x=fct_reorder(ROI, Mean_SUVR, .desc=F),
y=Mean_SUVR, fill=Mean_SUVR,
label = ROI)) +
geom_bar(stat="identity", show.legend=F) +
geom_errorbar(aes(ymin=ymin, ymax=ymax), width=0) +
coord_flip() +
theme_minimal() +
ylab("Mean Tau-PET SUVR") +
xlab("Region of Interest") +
ggtitle("Mean Tau-PET SUVR by ROI")
ggplotly(p_roi_suvr, height=1000, width=600, tooltip=c("label", "y"))